Cellular Wave Computing in Nanoscale via Million Processor Chips

  • Tamás Roska
  • Laszlo Belady
  • Maria Ercsey-Ravasz


A bifurcation is emerging in computer science and engineering due to the sudden emergence of many-core or even kilo-processor chips on the market. Due to the physical limitations, in CMOS technologies below 65 nm, a drastic power dissipation limit, a major signal propagation speed and distance limit, and a distributed character of the circuit elements are forcing new architectures. As a result, locality, the local connectedness becomes a prevailing property, the cellular, i.e., mainly locally connected processor arrays are becoming the norm, and the cellular wave dynamics can produce unique and practical effects.

In this new world, new principles are needed and new design methodologies. Luckily, the 15 years of research and development in cellular wave computing and CNN technology, we have aquired skills that help establishing some principles and techniques that might lead toward a new computer science and technology in designing mega-processor systems from kilo-processor chips.

In this chapter, we review the architectural development from standard CNN dynamics to the Cellular Wave Computer, showing several practical implementations, introduce the basic concepts of the Virtual Cellular Machine, present a new kind of implementation combining spatial-temporal algorithms with physics, give some architectural principles for non-CMOS implementations, and comment on biological relevance.


Image Flow Black Pixel Disjunctive Normal Form Processor Array Natural Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The supports of the Office of Naval Research, the Future and Emerging Technology program of the EU, the Computer and Automation Research Institute of the Hungarian Academy of Sciences, the Hungarian National Research Fund (OTKA), the Pázmány P. Catholic University, Budapest, the University of California at Berkeley, and the University of Notre Dame are gratefully acknowledged.


  1. Bálya, D, Petrás I, Roska T, Carmona R, Rodríguez-Vázquez Á (2004) Implementing the multilayer retinal model on the complex-cell CNN-UM chip prototype. Int J Bifurcation Chaos 14:427–451CrossRefGoogle Scholar
  2. Bálya D, Roska B, Roska T, Werblin FS (2002) A CNN framework for modeling parallel processing in the mammalian retina. Int J Circuit Theor Appl 30:363–393MATHCrossRefGoogle Scholar
  3. Chua LO (1999) A paradigm for complexity. World Scientific, New York, SingaporeGoogle Scholar
  4. Chua LO, Roska T (2002) Cellular neural networks and visual computing. Cambridge University Press, Cambridge, UKCrossRefGoogle Scholar
  5. de-Souza SX, Suykens JAK, Vandewalle J (2006) Learning of spatiotemporal behavior in cellular neural networks. Int J Circuit Theor Appl 34:127–140Google Scholar
  6. Ercsey-Ravasz M, Roska T, Néda Z (2006) Stochastic simulations on the cellular wave computers. Eur Phys J B 51:407–412CrossRefGoogle Scholar
  7. Fodróczi Z, Radványi A (2006) Computational auditory scene analysis in cellular wave computing framework. Int J Circuit Theor Appl 34:489–515MATHCrossRefGoogle Scholar
  8. Halfhill TR (2007) Faster than a blink. Microprocessor,, 2/12/07, 2007
  9. ITRS (2007) International technology roadmap for semiconductors 2003, 2005, 2007Google Scholar
  10. Kék L, Karacs K, Zarándy Á, Roska T (2007) CNN template and subroutine library for cellular wave computing. Report DNS -1 – 2007, Computer and Automation Research Institute of the Hungarian Academy of Sciences, BudapestGoogle Scholar
  11. Kunz R, Tetzlaff R, Wolf D (2000) Brain electrical activity in epilepsy characterization of the spatio-temporal dynamics with cellular neural networks based on a correlation dimension analysis. IEEE Int Symp Circuits Syst (ISCAS 00)Google Scholar
  12. Mozsáry A, et al (2007) Function-in-layout: a demonstration with bio-inspired hyperacuity chip. Int J Circuit Theor Appl 35(3):149–164CrossRefGoogle Scholar
  13. Porod W, et al (2004) Bioinspired nano-sensor enhanced CNN visual computer. In Roco MC, Montemagno C (eds) The coevolution of human potential and converging technologies. Ann NY Acad Sci 1013:92–109CrossRefGoogle Scholar
  14. Rekeczky CS, Szatmári I, Bálya D, Timár G, Zarándy Á (2004) Cellular multiadaptive analogic architecture: a computational framework for UAV applications. IEEE Transact Circuits Syst I 51:864–884CrossRefGoogle Scholar
  15. Rodriguez-Vázquez A, Linan Cembrano G, et al (2004) ACE 16 k: The third generation of mixed signal SIMD CNN ACE chips toward VsoCs. IEEE Transact Circuits Syst I 51:851–863CrossRefGoogle Scholar
  16. Roska B, Werblin FS (2001) Vertical interactions across ten parallel, stacked representations in the mammalian retina. Nature 410:583–587 (see also in Scientific American, April, 2007)Google Scholar
  17. Roska T (2003) Computational and computer complexity of analogic cellular wave computers. J Circuits Syst Comput 5(2):539–562CrossRefGoogle Scholar
  18. Roska T (2005) Cellular wave computers for brain-like spatial-temporal sensory computing. IEEE Circuits Syst Magazine 19(2): 5–19CrossRefGoogle Scholar
  19. Roska T (2007a) Cellular wave computers for nano-tera-scale technology – beyond boolean, spatial-temporal logic in million processor devices. Electron Lett 43:427–429 (Insight Letter)Google Scholar
  20. Roska T (2007b) Circuits, computers, and beyond boolean logic. Int J Circuit Theor Appl 35: 427–429CrossRefGoogle Scholar
  21. Roska T, Chua LO (1993) The CNN Universal Machine – an analogic array computer. IEEE Transact Circuits Syst II 40:163–173MathSciNetMATHCrossRefGoogle Scholar
  22. Szatmári I (2006) Object comparison using PDE-based wave metric on cellular neural networks. ibid, vol. 34, pp. 359–382, 2006.Google Scholar
  23. Tetzlaff R, Niederhöfer Ch, Fischer Ph (2006) Automated detection of a preseizure state: non-linear EEG analysis in epilepsy by cellular nonlinear networks and volterra systems. Int J Circuit Theor Appl 34: 89–108MATHCrossRefGoogle Scholar
  24. Turing A (1952) The chemical basis of morphogenesis. Phil Trans R Soc Lond 237B:37–72Google Scholar
  25. Von Neumann J (1987) Papers of John von Neumann on computing and computer theory. In Aspray W, Burks A (eds) Section IV: Theory of natural and artificial automata. The MIT Press and Tomash Publications, Los Angeles/San FranciscoGoogle Scholar
  26. Zarándy Á, Dominguez-Castro R, Espejo S (2002) Ultra-high frame rate focal plane image sensor and processor. IEEE Sensors J 2:559–565CrossRefGoogle Scholar
  27. Zarándy Á, Rekeczky CS (2005) Bi-i: A standalone ultra high speed cellular vision system. IEEE Circuits Syst Magazine 5(2):36–45CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Tamás Roska
    • 1
  • Laszlo Belady
    • 2
  • Maria Ercsey-Ravasz
    • 3
  1. 1.Computer and Automation Institute of the Hungarian Academy of Sciences and the Faculty of Information Technology of the Pázmány UniversityBudapestHungary
  2. 2.Eutecus Inc.BerkeleyU.S.A.
  3. 3.University of Notre DameNotre DameU.S.A.

Personalised recommendations